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Description
I am running Multinomial VAE on movielens 1m dataset as given in the example notebook but getting an error when fitting the model. The line of code is -
with Timer() as t: model_without_anneal.fit(x_train=train_data, x_valid=val_data, x_val_tr=val_data_tr, x_val_te=val_data_te_ratings, # with the original ratings mapper=am_val ) print("Took {} seconds for training.".format(t))
and the corresponding error is -
Error message - TypeError: You are passing KerasTensor(type_spec=TensorSpec(shape=(), dtype=tf.float32, name=None), name='Placeholder:0', description="created by layer 'tf.cast_8'"), an intermediate Keras symbolic input/output, to a TF API that does not allow registering custom dispatchers, such as tf.cond, tf.function, gradient tapes, or tf.map_fn. Keras Functional model construction only supports TF API calls that do support dispatching, such as tf.math.add or tf.reshape. Other APIs cannot be called directly on symbolic Kerasinputs/outputs. You can work around this limitation by putting the operation in a custom Keras layer call and calling that layer on this symbolic input/output.
Please guide what can be done about this. Thank you.